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First AI model that translates 100 languages without relying on English
- dvduval 6y agoNot currently possessing the skill necessary to get that library up and running, is there a way I can just test it and see how it translates?
- sixhobbits 6y agoI guess by posting on Facebook? It's not clear from the post whether they are running this yet. Otherwise maybe https://huggingface.co/ https://huggingface.co/ or similar might release a version with interface.
- jimmydorry 6y agoThis is a pretty huge milestone. However, I really want to see the next generation of MTL. From what I've seen, we're still a long way away from natural sounding translations. Some pairs are also very bad right now, particularly Korean->English.
- karmasimida 6y agoYou can try this service (developed by a South Korean company), it is miles better than Google Translate IMO: https://papago.naver.com/ https://papago.naver.com/
- jimmydorry 6y agoThanks, i’ll have a look.
- whimsicalism 6y ago> From what I've seen, we're still a long way away from natural sounding translations. IMO, this is a dataset problem. I think we're finally seeing the "next generation" of dataset collection in AI so there will be hopefully improvements for pairs with sparser sets.
- jimmydorry 6y agoVery true. These kinds of problems centre around data set acquisition and validation.
- progfix 6y ago> we're still a long way away from natural sounding translations. DeepL makes perfect translations between English and German. Perfect in a sense that it looks like a professional translator translated it. Google, Microsoft, Facebook might be far off, but deepl isn't.
- nestorD 6y agoNot perfect between English and French but certainly great. The possibility to insert your corrections in the suggested translation and let the algorithm rewrite the following text make it a huge time saver when one needs to do a translation.
- bloak 6y ago"Natural-sounding" is much easier to achieve than "correct".
- jimmydorry 6y agoIs it though? You can construct English sentences that are hard to read, but grammatically correct.
- vsskanth 6y agoWould appreciate it if someone can ELI5 how they assembled a dataset for this task, without relying on english. I am not able to figure out their LASER 2.0 system
- orange3xchicken 6y agoThey do rely on english & spanish translations to construct the dataset. They have existing tools to jointly embed sentences from multiple languages (laser). These models are trained using parallel corpora involving either english or spanish on a translation task. Using these models and the joint embeddings they produce, fb can mine the web for new pairs by roughly identifying whether two sentences in two different languages correspond to a translation pair.
- whimsicalism 6y agoOf course, the problem with these "synthetic" datasets are when the translation models overfit to the problems in these multilingual sentence encoders/embedders. Still, it lets you massively increase the amount of data you're training on, which is generally worth it.
- onurcel 6y agoExactly. I work on Laser2, the approach is the same as Laser [0], Laser2 performs better on some low resource languages. A more ELI5 explanation would be something like this: Laser is an encoder/decoder architecture trained as a translation task from language X to english/spanish, with the particularity of having only one vector between the encoder and decoder. Once this system is trained (with public translation datasets), the vector that the encoder gives you for an input sentence represents the "meaning" of that sentence, since the decoder relies only on that information to generate a translation. And this vector representation is the same for any language the system was trained on. So we use that system to mine data from commoncrawl: giving any language pair (say romanian-nepali), having two vectors close to each other in that latent space means the sentences have the same meaning. We use fastText's language classifier [1] to filter from commoncrawl, compute the vector representations with Laser, and find close vectors thanks to Faiss [2]. [0] https://engineering.fb.com/ai-research/laser-multilingual-sentence-embeddings/ https://engineering.fb.com/ai-research/laser-multilingual-se... [1] https://fasttext.cc/docs/en/language-identification.html https://fasttext.cc/docs/en/language-identification.html [2] https://github.com/facebookresearch/faiss https://github.com/facebookresearch/faiss
- fireattack 6y agoI know DeepL doesn't support 100 languages, but does it rely on English?
- timonoko 6y agoEasy to test. "Koirissanikohan" should be a sentence (or a word) conveying exactly this meaning: "I wonder if also probably in my dogs there is ..". Will not happen in pure AI-based system without hard-coded parsing rules and without changing the obviously default dictionary-based english-like language model.
- canjobear 6y agoModern machine translation systems include character-based models that can capture the structure within words.
- timonoko 6y agoThere is no rigid structure as the spelling is adjusted to make it easier to pronounce. I say pure text-reading AI would never learn all nuances and rules on its own, because so much is lost in translation.
- nextaccountic 6y agoThen maybe the training data shouldn't be based only on translations? Maybe some texts that describe a given language should also be used. This doesn't seem an insurmountable problem.
- doikor 6y agoProblem is that we Finns can use a word (a known base word and adding the inflection to get the wanted meaning) in a sentence that has never been said or written before and the native speakers will still understand it. Basically a full training set with all the words does not exist. Even less a set with actual translations for them. This also means that autocomplete etc is pretty much useless for us. I just disable it on iPhone as it actively makes typing out a sentence harder. (also to make things harder as others have pointed out the base word and inflections get changed to make the word easier to pronounce so there is no static form for them when you stack them)
- tellarin 6y agoThis claim seems a bit exaggerated. Microsoft Research Asia, as one example, has been doing neural machine translation for a large number of languages without relying in English for quite some time; and models are used in production scenarios too.
- bredren 6y agoWhen I was doing my MBA about a decade ago, I teamed up with two other grad students to propose, raise money and ultimately install a demonstration / residential size wind turbine on campus. It was part of our work positioning entrepreneurship around clean energy. We worked with the college press office to draft our press release, and in it we made the claim that it was the first wind turbine on a school campus in the greater Boston area. Well one of the bigger papers picked up the story and the press office ended up getting a nasty voicemail from some parent at a private high school in some Boston suburb. The parent wanted our school to know that HIS kid's school was the first to put up a wind turbine. This actually worried our little team because we didn't want to say something untrue. But the pr person explained that this "first to do X" type of thing is a very common press tactic and it is not unusual to have your claim disputed. And that second, "greater Boston area" is not really a narrowly defined term. This was something of a relief and we sort of had a running gag about the PTA dad with sour grapes about our wind turbine from then on.
- deleted 6y ago[deleted]
- Ma8ee 6y agoIt reminds me of a church which claim to fame is being the second largest wooden church in the middle part of northern Sweden.
- aquadrop 6y agoSorry, but what your PR office's response looks like is just a more elaborate way of saying "Don't worry, we lie all the time"
- MichaelMoser123 6y agoi was using the automatic translation for Mongolian on facebook recently, and I couldn't make much sense out of the output. Interesting what kind of translation software was being used there.
- visarga 6y agoMost of the models announced in papers can't be deployed in production because they are not optimised for inference efficiency. For example Google Translate the public service is not as good as the SOTA papers.
- MichaelMoser123 6y agothat's interesting; are these models used in any other capacity? Is there any other application/context where the models are being used?
- visarga 6y agoJust for research. Most research models are not used for anything. Imagine having to serve a model that requires 2 x 32GB GPU to billions of users. Text to speech is also much worse in deployment compared to research. The recent research models have much better intonation. GPT-3 is the worst offender here - it's so big that it becomes almost uneconomical to run, and certainly impossible to offer for free. (estimated requirements are 11 Tesla V100 GPUs)
- MichaelMoser123 6y agoThey could have sold this as a service to those who need higher quality translation services; think about the wasted gpu time required to build the models. (Also laymen like me would think that all the effort is being wasted) also practical usage is also a kind of test, isn't it?
- iggldiggl 6y agoBetween German and English one example that stuck with me was it confusing "farmer" and "builder" because in German, within compound words both of these map to "-bauer". Cue a number of auto-translated job adverts advertising positions as a "street farmer" and suchlike…
- fomine3 6y agoNow machine translation generates fluent text (at least for en-ja), but my dissatisfaction for machine translation is that it sometimes miss negative word that's very important for information. IMO don't miss negate word is important than fluent.
- spi 6y agoI fully agree with this. As a data scientist, I always think that this is a "natural" consequence of one of the main (if not _the_ main) metric used to evaluate machine translation algorithms, which is BLEU: https://en.wikipedia.org/wiki/BLEU https://en.wikipedia.org/wiki/BLEU According to this metric, if you have a moderately long sentence like "I am not the person who said the president should be reelected" and your translation missed the "not", you would still get a score of 11/12 ~ 92%. And, as far as I know, word order doesn't even matter, so "I am the person who said the president should not be reelected", while wrong, would get a perfect score. Of course these are rather artificial examples, and in general machine translation algorithms and their evaluation work because it's "easier" to create an algorithm that gets the right translation than one that, unintentionally, fools the metric systematically. Nevertheless if the research community used a metric that punished this kind of mistakes more strongly, I suspect that over time a few new algorithms could come up that improve on this specific point. Alas, I don't know of any such metric (nor I would know how to design one, of course, otherwise I'd publish it ;-) ).
- whimsicalism 6y agoBLEU is not what is being optimized here. There are plenty of alternative scoring metrics, WMT has a competition on it every year. It is also just false that BLEU does not care about order.
- danielscrubs 6y agoI tried using en-ja on Google Translate for: "fall through the cracks". It's an extremely common idiom and I used it in a proper context. It fails, horribly. I don't know why I even bother checking on Google Translate. It basically fails every single time to create natural Japanese. Our teachers could tell in a second if it was made by Google Translate. Just don't rely on it. Ever.
- peter303 6y agoStar Trek Universal Translator. One day it will tell us what Mark Z is really saying.
- pjmlp 6y agoI am yet to find a service that properly translates to European Portuguese with proper use of pronoums, so no big hopes here.
- akie 6y agoI'm guessing you've had a look at https://www.deepl.com/translator https://www.deepl.com/translator ? I don't speak Portuguese so I can't judge, but it explicitly offers European Portuguese as a translation target, and their other translations are usually top-notch.
- YeGoblynQueenne 6y agoThe service you link to doesn't seem to offer a translation from and to Greek. It's also missing many other EU languages, e.g. Swedish, Danish, Hungarian, Zcech, etc. Edit: to clarify, other translation servies I've used, notably Google, do translate from and to Greek but make a meal out of it.
- psychoslave 6y agoFrom https://www.theatlantic.com/technology/archive/2018/01/the-shallowness-of-google-translate/551570/ https://www.theatlantic.com/technology/archive/2018/01/the-s... Douglas Hofstadter's test sentence: In their house, everything comes in pairs. There’s his car and her car, his towels and her towels, and his library and hers. DeepL translation: Dans leur maison, tout vient par deux. Il y a sa voiture et la sienne, ses serviettes et les siennes, sa bibliothèque et la sienne. DH translation: Chez eux, ils ont tout en double. Il y a sa voiture à elle et sa voiture à lui, ses serviettes à elle et ses serviettes à lui, sa bibliothèque à elle et sa bibliothèque à lui. As a French native, I can tell you that DH's translation is great. And DeepL's reply is still something you can recognize at automatic translation at first sight. Don’t get me wrong, more often than not, current MT tools give good enough results to understand the treated topic. But that’s still hideous translations. I wouldn’t buy a book translated through them for example.
- jfengel 6y ago
- heraclius 6y ago> In the example above, Hindi, Bengali, and Tamil would be bridge languages for Indo-Aryan languages. Tamil isn’t an Indo-Aryan language. It would be rather surprising if it were a good bridge for any two Indo-Aryan langauges.
- marcbarbosa 6y agoIt's odd (or funny) that in the explanatory video about a high advanced AI model for translations it has a misspelling: Portugese
- wizzwizz4 6y agoIt should be Portugeese. https://mandrillcomics.com/comic/43/page/80 https://mandrillcomics.com/comic/43/page/80
- bufferoverflow 6y agoThe model itself is 136GB https://dl.fbaipublicfiles.com/m2m_100/12b_last_checkpoint.pt https://dl.fbaipublicfiles.com/m2m_100/12b_last_checkpoint.p... I can't imagine the amount of computation it took to generate this.
- deleted 6y ago[deleted]
- grumple 6y agoGoogle beat them by 4 years, what are they on about? [1] 1. https://en.wikipedia.org/wiki/Google_Neural_Machine_Translation https://en.wikipedia.org/wiki/Google_Neural_Machine_Translat...
- stlee42 6y agoHere is the list of languages: Afrikaans Albanian Amharic Arabic Armenian Asturian Azerbaijani Bashkir Basque Belarusian Bengali Bosnian Breton Bulgarian Burmese Catalan Cebuano Chinese Croatian Czech Danish Dutch Eastern Punjabi English Estonian Finnish French Fulah Galician Ganda Georgian German Greek Gujarati Haitian Hausa Hebrew Hindi Hungarian Icelandic Igbo Ilokano Indonesian Irish Italian Japanese Javanese Kannada Kazakh Khmer Korean Lao Latvian Lingala Lithuanian Luxembourgish Macedonian Malagasy Malay Malayalam Marathi Mongolian Nepali Northern Sotho Norwegian (Bokmål) Occitan Oriya Oromo Pashto Persian Polish Portuguese Romanian Russian Scottish Gaelic Serbian Sindhi Sinhalese Slovak Slovenian Somali Spanish Sundanese Swahili Swati Swedish Tagalog Tamil Thai Tswana Turkish Ukrainian Urdu Uzbek Vietnamese Welsh West Frisian Wolof Xhosa Yiddish Yoruba Zulu